{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:57:59.309632Z",
     "start_time": "2020-10-18T01:57:59.294631Z"
    }
   },
   "outputs": [],
   "source": [
    "# 不要显示警告信息\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# 设置可以在notebook中可以显示的最大行数和最大列数\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:43.328727Z",
     "start_time": "2020-10-18T00:21:30.144973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_total_num</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>order_total_payment</th>\n",
       "      <th>order_total_discount</th>\n",
       "      <th>order_pay_time</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_count</th>\n",
       "      <th>is_customer_rate</th>\n",
       "      <th>order_detail_status</th>\n",
       "      <th>order_detail_goods_num</th>\n",
       "      <th>order_detail_amount</th>\n",
       "      <th>order_detail_payment</th>\n",
       "      <th>order_detail_discount</th>\n",
       "      <th>customer_province</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>member_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_gender</th>\n",
       "      <th>member_status</th>\n",
       "      <th>is_member_actived</th>\n",
       "      <th>goods_id</th>\n",
       "      <th>goods_class_id</th>\n",
       "      <th>goods_price</th>\n",
       "      <th>goods_status</th>\n",
       "      <th>goods_has_discount</th>\n",
       "      <th>goods_list_time</th>\n",
       "      <th>goods_delist_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>239.9</td>\n",
       "      <td>96.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-11-01 00:10:56</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.9</td>\n",
       "      <td>96.9</td>\n",
       "      <td>143.0</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>54.909289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-25 11:08:07</td>\n",
       "      <td>2014-11-01 11:08:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001530</td>\n",
       "      <td>1001327</td>\n",
       "      <td>2.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>96.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 23:14:42</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>鄂尔多斯市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1953</td>\n",
       "      <td>1953</td>\n",
       "      <td>45.961352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-08-28 17:27:50</td>\n",
       "      <td>2013-09-01 00:38:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001531</td>\n",
       "      <td>1001327</td>\n",
       "      <td>2.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>96.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 23:14:42</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.9</td>\n",
       "      <td>96.9</td>\n",
       "      <td>92.1</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>鄂尔多斯市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1083</td>\n",
       "      <td>1083</td>\n",
       "      <td>53.035439</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-29 18:21:05</td>\n",
       "      <td>2014-11-05 18:21:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001532</td>\n",
       "      <td>1001328</td>\n",
       "      <td>3.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>89.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 22:06:35</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>89.7</td>\n",
       "      <td>89.7</td>\n",
       "      <td>90.3</td>\n",
       "      <td>浙江省</td>\n",
       "      <td>杭州市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1013</td>\n",
       "      <td>1013</td>\n",
       "      <td>46.046917</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014-10-25 11:00:00</td>\n",
       "      <td>2014-11-01 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001533</td>\n",
       "      <td>1001329</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159.9</td>\n",
       "      <td>65.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 21:33:36</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.9</td>\n",
       "      <td>65.9</td>\n",
       "      <td>94.0</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1628</td>\n",
       "      <td>1628</td>\n",
       "      <td>50.722161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-23 15:35:33</td>\n",
       "      <td>2014-10-30 15:35:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1001534</td>\n",
       "      <td>1001330</td>\n",
       "      <td>1.0</td>\n",
       "      <td>129.9</td>\n",
       "      <td>66.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 20:57:08</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>66.9</td>\n",
       "      <td>63.0</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1038</td>\n",
       "      <td>1038</td>\n",
       "      <td>49.408291</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-28 14:26:28</td>\n",
       "      <td>2014-11-04 14:26:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1001535</td>\n",
       "      <td>1001331</td>\n",
       "      <td>1.0</td>\n",
       "      <td>239.9</td>\n",
       "      <td>89.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 20:21:05</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.9</td>\n",
       "      <td>89.9</td>\n",
       "      <td>150.0</td>\n",
       "      <td>甘肃省</td>\n",
       "      <td>金昌市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>54.850109</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-25 11:08:07</td>\n",
       "      <td>2014-11-01 11:08:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1001536</td>\n",
       "      <td>1001332</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.9</td>\n",
       "      <td>115.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 19:49:50</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.9</td>\n",
       "      <td>115.9</td>\n",
       "      <td>174.0</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>苏州市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1858</td>\n",
       "      <td>1858</td>\n",
       "      <td>56.745159</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-24 23:59:59</td>\n",
       "      <td>2014-10-31 23:59:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001537</td>\n",
       "      <td>1001333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>159.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 19:11:24</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>59.9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>包头市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1943</td>\n",
       "      <td>1943</td>\n",
       "      <td>50.808977</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-24 00:00:00</td>\n",
       "      <td>2014-10-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1001538</td>\n",
       "      <td>1001334</td>\n",
       "      <td>1.0</td>\n",
       "      <td>129.9</td>\n",
       "      <td>66.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-31 18:31:05</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>66.9</td>\n",
       "      <td>63.0</td>\n",
       "      <td>辽宁省</td>\n",
       "      <td>朝阳市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1001331</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1038</td>\n",
       "      <td>1038</td>\n",
       "      <td>49.592109</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-28 14:26:28</td>\n",
       "      <td>2014-11-04 14:26:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_detail_id  order_id  order_total_num  order_amount  \\\n",
       "0          1000000   1000000              1.0         239.9   \n",
       "1          1001530   1001327              2.0         288.0   \n",
       "2          1001531   1001327              2.0         288.0   \n",
       "3          1001532   1001328              3.0         180.0   \n",
       "4          1001533   1001329              1.0         159.9   \n",
       "5          1001534   1001330              1.0         129.9   \n",
       "6          1001535   1001331              1.0         239.9   \n",
       "7          1001536   1001332              1.0         289.9   \n",
       "8          1001537   1001333              1.0         159.9   \n",
       "9          1001538   1001334              1.0         129.9   \n",
       "\n",
       "   order_total_payment  order_total_discount       order_pay_time  \\\n",
       "0                 96.9                   0.0  2012-11-01 00:10:56   \n",
       "1                 96.9                   0.0  2013-08-31 23:14:42   \n",
       "2                 96.9                   0.0  2013-08-31 23:14:42   \n",
       "3                 89.7                   0.0  2013-08-31 22:06:35   \n",
       "4                 65.9                   0.0  2013-08-31 21:33:36   \n",
       "5                 66.9                   0.0  2013-08-31 20:57:08   \n",
       "6                 89.9                   0.0  2013-08-31 20:21:05   \n",
       "7                115.9                   0.0  2013-08-31 19:49:50   \n",
       "8                 59.9                   0.0  2013-08-31 19:11:24   \n",
       "9                 66.9                   0.0  2013-08-31 18:31:05   \n",
       "\n",
       "   order_status  order_count  is_customer_rate  order_detail_status  \\\n",
       "0             6          1.0               0.0                  6.0   \n",
       "1             6          2.0               0.0                  6.0   \n",
       "2             6          2.0               0.0                  6.0   \n",
       "3             6          1.0               0.0                  6.0   \n",
       "4             6          1.0               0.0                  6.0   \n",
       "5             6          1.0               0.0                  6.0   \n",
       "6             6          1.0               0.0                  6.0   \n",
       "7             6          1.0               0.0                  6.0   \n",
       "8             6          1.0               0.0                  6.0   \n",
       "9             6          1.0               0.0                  6.0   \n",
       "\n",
       "   order_detail_goods_num  order_detail_amount  order_detail_payment  \\\n",
       "0                     1.0                 96.9                  96.9   \n",
       "1                     1.0                  0.0                   0.0   \n",
       "2                     1.0                 96.9                  96.9   \n",
       "3                     3.0                 89.7                  89.7   \n",
       "4                     1.0                 65.9                  65.9   \n",
       "5                     1.0                 66.9                  66.9   \n",
       "6                     1.0                 89.9                  89.9   \n",
       "7                     1.0                115.9                 115.9   \n",
       "8                     1.0                 59.9                  59.9   \n",
       "9                     1.0                 66.9                  66.9   \n",
       "\n",
       "   order_detail_discount customer_province customer_city  member_id  \\\n",
       "0                  143.0                北京           北京市        0.0   \n",
       "1                   99.0            内蒙古自治区         鄂尔多斯市        0.0   \n",
       "2                   92.1            内蒙古自治区         鄂尔多斯市        0.0   \n",
       "3                   90.3               浙江省           杭州市        0.0   \n",
       "4                   94.0                北京           北京市        0.0   \n",
       "5                   63.0                北京           北京市        0.0   \n",
       "6                  150.0               甘肃省           金昌市        0.0   \n",
       "7                  174.0               江苏省           苏州市        0.0   \n",
       "8                  100.0            内蒙古自治区           包头市        0.0   \n",
       "9                   63.0               辽宁省           朝阳市        0.0   \n",
       "\n",
       "   customer_id  customer_gender  member_status  is_member_actived  goods_id  \\\n",
       "0      1000000              NaN            NaN                NaN       998   \n",
       "1      1001324              NaN            NaN                NaN      1953   \n",
       "2      1001324              NaN            NaN                NaN      1083   \n",
       "3      1001325              NaN            NaN                NaN      1013   \n",
       "4      1001326              NaN            NaN                NaN      1628   \n",
       "5      1001327              NaN            NaN                NaN      1038   \n",
       "6      1001328              NaN            NaN                NaN       998   \n",
       "7      1001329              NaN            NaN                NaN      1858   \n",
       "8      1001330              NaN            NaN                NaN      1943   \n",
       "9      1001331              NaN            NaN                NaN      1038   \n",
       "\n",
       "   goods_class_id  goods_price  goods_status  goods_has_discount  \\\n",
       "0             998    54.909289           1.0                 0.0   \n",
       "1            1953    45.961352           0.0                 1.0   \n",
       "2            1083    53.035439           1.0                 0.0   \n",
       "3            1013    46.046917           1.0                 1.0   \n",
       "4            1628    50.722161           1.0                 0.0   \n",
       "5            1038    49.408291           1.0                 0.0   \n",
       "6             998    54.850109           1.0                 0.0   \n",
       "7            1858    56.745159           1.0                 0.0   \n",
       "8            1943    50.808977           1.0                 0.0   \n",
       "9            1038    49.592109           1.0                 0.0   \n",
       "\n",
       "       goods_list_time    goods_delist_time  \n",
       "0  2014-10-25 11:08:07  2014-11-01 11:08:07  \n",
       "1  2013-08-28 17:27:50  2013-09-01 00:38:17  \n",
       "2  2014-10-29 18:21:05  2014-11-05 18:21:05  \n",
       "3  2014-10-25 11:00:00  2014-11-01 11:00:00  \n",
       "4  2014-10-23 15:35:33  2014-10-30 15:35:33  \n",
       "5  2014-10-28 14:26:28  2014-11-04 14:26:28  \n",
       "6  2014-10-25 11:08:07  2014-11-01 11:08:07  \n",
       "7  2014-10-24 23:59:59  2014-10-31 23:59:59  \n",
       "8  2014-10-24 00:00:00  2014-10-31 00:00:00  \n",
       "9  2014-10-28 14:26:28  2014-11-04 14:26:28  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/train.csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train.csv字段说明  \n",
    "- order_detail_id\t订单详情id\n",
    "- order_id\t订单id\n",
    "- order_total_num\t订单商品总购买数量\n",
    "- order_amount\t订单商品总金额\n",
    "- order_total_payment\t订单实付金额\n",
    "- order_total_discount\t订单优惠金额\n",
    "- order_pay_time\t付款时间\n",
    "- order_status\t订单状态： 1表示等待买家付款， 2表示卖家部分发货， 3表示卖家发货， 4表示等待买家确认收货， 5表示买家已签收， 6表示交易成功\n",
    "- order_count\t订单包含的子订单数量\n",
    "- is_customer_rate\t用户是否评价，0没有评价，1已经评价\n",
    "- order_detail_status\t订单详细状态\n",
    "- order_detail_good_num\t订单中的商品数量\n",
    "- order_detail_amount\t订单应付总金额\n",
    "- order_detail_payment\t订单实付金额\n",
    "- order_detail_discount\t订单优惠金额\n",
    "- member_id\t会员id\n",
    "- customer_id\t用户id\n",
    "- customer_gender\t性别：0未知，1男，2女\n",
    "- customer_province\t用户省份所在地\n",
    "- customer_city\t用户城市所在地\n",
    "- member_status\t会员状态：1正常，2冻结，3已删除\n",
    "- is_member_active\t会员是否激活：0没有激活，1已激活\n",
    "- goods_id\t商品id\n",
    "- goods_class_id\t商品分类id\n",
    "- goods_price\t商品原始价格\n",
    "- goods_status\t商品库存状态：1出售中，2库中\n",
    "- goods_has_discount\t是否支持会员折扣：0不支持，1支持\n",
    "- goods_list_time\t商品最新上架时间\n",
    "- goods_delist_time\t商品最新下架时间  \n",
    "\n",
    "submission.csv 字段说明\n",
    "- customer_id\t用户id\n",
    "- result\t下个月是否会购买：0 不购买，1购买"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查数据是否有重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:39:19.415929Z",
     "start_time": "2020-10-18T00:39:17.023792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data.duplicated(subset=(['order_id','order_detail_id'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:40:00.298267Z",
     "start_time": "2020-10-18T00:39:58.542167Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "387196"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data.duplicated(subset=(['order_id'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:40:31.288040Z",
     "start_time": "2020-10-18T00:40:29.489937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data.duplicated(subset=(['order_detail_id'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:45:11.688078Z",
     "start_time": "2020-10-18T00:45:11.677077Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2306871, 29)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:43.347728Z",
     "start_time": "2020-10-18T00:21:43.330727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2306871 entries, 0 to 2306870\n",
      "Data columns (total 29 columns):\n",
      " #   Column                  Dtype  \n",
      "---  ------                  -----  \n",
      " 0   order_detail_id         int64  \n",
      " 1   order_id                int64  \n",
      " 2   order_total_num         float64\n",
      " 3   order_amount            float64\n",
      " 4   order_total_payment     float64\n",
      " 5   order_total_discount    float64\n",
      " 6   order_pay_time          object \n",
      " 7   order_status            int64  \n",
      " 8   order_count             float64\n",
      " 9   is_customer_rate        float64\n",
      " 10  order_detail_status     float64\n",
      " 11  order_detail_goods_num  float64\n",
      " 12  order_detail_amount     float64\n",
      " 13  order_detail_payment    float64\n",
      " 14  order_detail_discount   float64\n",
      " 15  customer_province       object \n",
      " 16  customer_city           object \n",
      " 17  member_id               float64\n",
      " 18  customer_id             int64  \n",
      " 19  customer_gender         float64\n",
      " 20  member_status           float64\n",
      " 21  is_member_actived       float64\n",
      " 22  goods_id                int64  \n",
      " 23  goods_class_id          int64  \n",
      " 24  goods_price             float64\n",
      " 25  goods_status            float64\n",
      " 26  goods_has_discount      float64\n",
      " 27  goods_list_time         object \n",
      " 28  goods_delist_time       object \n",
      "dtypes: float64(18), int64(6), object(5)\n",
      "memory usage: 510.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:44.473792Z",
     "start_time": "2020-10-18T00:21:43.349728Z"
    }
   },
   "outputs": [],
   "source": [
    "#通过观察，下面的特征属于类型，作为object类型处理更合适些\n",
    "data.order_status = data.order_status.astype(object)\n",
    "data.is_customer_rate = data.is_customer_rate.astype(object)\n",
    "data.order_detail_status = data.order_detail_status.astype(object)\n",
    "data.customer_gender = data.customer_gender.astype(object)\n",
    "data.member_status = data.customer_gender.astype(object)\n",
    "data.is_member_actived = data.is_member_actived.astype(object)\n",
    "data.goods_status = data.goods_status.astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:46.060883Z",
     "start_time": "2020-10-18T00:21:44.475792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>order_detail_id</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>2340042.52</td>\n",
       "      <td>774031.38</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1669684.50</td>\n",
       "      <td>2339350.00</td>\n",
       "      <td>3007430.50</td>\n",
       "      <td>3685499.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>2136151.70</td>\n",
       "      <td>650068.97</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1571484.50</td>\n",
       "      <td>2142988.00</td>\n",
       "      <td>2707954.00</td>\n",
       "      <td>3238361.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_total_num</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>1.68</td>\n",
       "      <td>3.94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_amount</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>310.80</td>\n",
       "      <td>404.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.00</td>\n",
       "      <td>239.90</td>\n",
       "      <td>339.80</td>\n",
       "      <td>102374.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_total_payment</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>111.47</td>\n",
       "      <td>164.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66.90</td>\n",
       "      <td>89.90</td>\n",
       "      <td>113.17</td>\n",
       "      <td>98401.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_total_discount</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>5.91</td>\n",
       "      <td>17.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1960.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_count</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>1.77</td>\n",
       "      <td>480.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>729061.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_detail_goods_num</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>1.18</td>\n",
       "      <td>3.57</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_detail_amount</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>86.64</td>\n",
       "      <td>128.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.90</td>\n",
       "      <td>83.90</td>\n",
       "      <td>98.90</td>\n",
       "      <td>98401.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_detail_payment</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>82.80</td>\n",
       "      <td>126.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55.90</td>\n",
       "      <td>79.90</td>\n",
       "      <td>93.90</td>\n",
       "      <td>98401.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_detail_discount</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>133.85</td>\n",
       "      <td>173.92</td>\n",
       "      <td>-101.16</td>\n",
       "      <td>89.10</td>\n",
       "      <td>130.10</td>\n",
       "      <td>150.00</td>\n",
       "      <td>79360.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>578983.22</td>\n",
       "      <td>1075381.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>483484.00</td>\n",
       "      <td>4161555.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>1933232.83</td>\n",
       "      <td>553888.52</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1436433.50</td>\n",
       "      <td>1929569.00</td>\n",
       "      <td>2457816.00</td>\n",
       "      <td>2826574.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_id</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>1558.21</td>\n",
       "      <td>780.33</td>\n",
       "      <td>998.00</td>\n",
       "      <td>1038.00</td>\n",
       "      <td>1233.00</td>\n",
       "      <td>1643.00</td>\n",
       "      <td>6673.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_class_id</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>1558.21</td>\n",
       "      <td>780.33</td>\n",
       "      <td>998.00</td>\n",
       "      <td>1038.00</td>\n",
       "      <td>1233.00</td>\n",
       "      <td>1643.00</td>\n",
       "      <td>6673.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_price</th>\n",
       "      <td>2306435.00</td>\n",
       "      <td>52.20</td>\n",
       "      <td>6.33</td>\n",
       "      <td>-79.97</td>\n",
       "      <td>49.60</td>\n",
       "      <td>53.91</td>\n",
       "      <td>54.85</td>\n",
       "      <td>92.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_has_discount</th>\n",
       "      <td>2306871.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count        mean         std         min  \\\n",
       "order_detail_id         2306871.00  2340042.52   774031.38  1000000.00   \n",
       "order_id                2306871.00  2136151.70   650068.97  1000000.00   \n",
       "order_total_num         2306871.00        1.68        3.94        1.00   \n",
       "order_amount            2306871.00      310.80      404.02        0.00   \n",
       "order_total_payment     2306871.00      111.47      164.41        0.00   \n",
       "order_total_discount    2306871.00        5.91       17.11        0.00   \n",
       "order_count             2306871.00        1.77      480.01        0.00   \n",
       "order_detail_goods_num  2306871.00        1.18        3.57        1.00   \n",
       "order_detail_amount     2306871.00       86.64      128.01        0.00   \n",
       "order_detail_payment    2306871.00       82.80      126.59        0.00   \n",
       "order_detail_discount   2306871.00      133.85      173.92     -101.16   \n",
       "member_id               2306871.00   578983.22  1075381.24        0.00   \n",
       "customer_id             2306871.00  1933232.83   553888.52  1000000.00   \n",
       "goods_id                2306871.00     1558.21      780.33      998.00   \n",
       "goods_class_id          2306871.00     1558.21      780.33      998.00   \n",
       "goods_price             2306435.00       52.20        6.33      -79.97   \n",
       "goods_has_discount      2306871.00        0.09        0.28        0.00   \n",
       "\n",
       "                               25%         50%         75%         max  \n",
       "order_detail_id         1669684.50  2339350.00  3007430.50  3685499.00  \n",
       "order_id                1571484.50  2142988.00  2707954.00  3238361.00  \n",
       "order_total_num               1.00        1.00        2.00     1700.00  \n",
       "order_amount                199.00      239.90      339.80   102374.00  \n",
       "order_total_payment          66.90       89.90      113.17    98401.50  \n",
       "order_total_discount          0.00        0.00        5.00     1960.00  \n",
       "order_count                   1.00        1.00        2.00   729061.00  \n",
       "order_detail_goods_num        1.00        1.00        1.00     1700.00  \n",
       "order_detail_amount          59.90       83.90       98.90    98401.50  \n",
       "order_detail_payment         55.90       79.90       93.90    98401.50  \n",
       "order_detail_discount        89.10      130.10      150.00    79360.00  \n",
       "member_id                     0.00        0.00   483484.00  4161555.00  \n",
       "customer_id             1436433.50  1929569.00  2457816.00  2826574.00  \n",
       "goods_id                   1038.00     1233.00     1643.00     6673.00  \n",
       "goods_class_id             1038.00     1233.00     1643.00     6673.00  \n",
       "goods_price                  49.60       53.91       54.85       92.35  \n",
       "goods_has_discount            0.00        0.00        0.00        1.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T.applymap(lambda x: format(x,'.2f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面对各个字段进行初步分析  \n",
    "- order_detail_id(订单详细id):  仅仅是id，没有分析必要\n",
    "- order_id(订单id): 仅仅是id，没有分析必要\n",
    "- order_total_num(订单商品总购买数量): 基本都是购买数量是1-2个，基本都是购买1个商品，由于一些大值拉大了平均值，最大值1700\n",
    "- order_amount(订单商品总金额) : 大部分订单金额处于199-239间，最大值102374\n",
    "- order_total_payment(订单实付金额) : 大部分购买金额处于66-113间\n",
    "- order_total_discount(订单优惠金额) : 大部分都优惠价格都是是0-5之间，均值是6\n",
    "- order_count(订单包含的子订单数量) : 大部分是1-2个，最大值729061\n",
    "- order_detail_goods_num(订单中的商品数量) : 大部分都是1,最大值1700\n",
    "- order_detail_amount(订单应付总金额): 大部分是59-98，最大值98401\n",
    "- order_detail_payment(订单实付金额) : 大部分是55-93，最大值98401，可能用了优惠券等，实付值区间比应付略微小些\n",
    "- order_detail_discount(订单优惠金额) : 大部分是89-150，最大值79360，最小值-101.16，这个负值得可能是异常值\n",
    "- goods_price(商品原始价格) :大部分是49-54，最大值92.35，最小值-79.97，这个负值得可能是异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:21:46.349900Z",
     "start_time": "2020-10-18T00:21:46.066883Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单ID</th>\n",
       "      <th>订单详细ID</th>\n",
       "      <th>订单商品总购买数量</th>\n",
       "      <th>订单商品总金额</th>\n",
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       "      <th>订单应付总金额</th>\n",
       "      <th>订单实付金额</th>\n",
       "      <th>订单优惠金额</th>\n",
       "      <th>商品原始价格</th>\n",
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       "  </thead>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>159.9</td>\n",
       "      <td>65.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.9</td>\n",
       "      <td>65.9</td>\n",
       "      <td>94.0</td>\n",
       "      <td>50.722161</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>59.9</td>\n",
       "      <td>139.1</td>\n",
       "      <td>53.012016</td>\n",
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       "    <tr>\n",
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       "      <td>3685496</td>\n",
       "      <td>2.0</td>\n",
       "      <td>299.8</td>\n",
       "      <td>89.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.9</td>\n",
       "      <td>42.693822</td>\n",
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       "    <tr>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>76.9</td>\n",
       "      <td>91.1</td>\n",
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       "      <td>52.1</td>\n",
       "      <td>46.114765</td>\n",
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       "<p>2306871 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            订单ID   订单详细ID  订单商品总购买数量  订单商品总金额  订单总实付金额  订单总优惠金额  订单包含的子订单数量  \\\n",
       "0        1000000  1000000        1.0    239.9     96.9      0.0         1.0   \n",
       "1        1001327  1001530        2.0    288.0     96.9      0.0         2.0   \n",
       "2        1001327  1001531        2.0    288.0     96.9      0.0         2.0   \n",
       "3        1001328  1001532        3.0    180.0     89.7      0.0         1.0   \n",
       "4        1001329  1001533        1.0    159.9     65.9      0.0         1.0   \n",
       "...          ...      ...        ...      ...      ...      ...         ...   \n",
       "2306866  3238358  3685495        1.0    199.0     59.9      0.0         1.0   \n",
       "2306867  3238359  3685496        2.0    299.8     89.9      0.0         2.0   \n",
       "2306868  3238359  3685497        2.0    299.8     89.9      0.0         2.0   \n",
       "2306869  3238360  3685498        1.0    168.0     76.9      0.0         1.0   \n",
       "2306870  3238361  3685499        1.0    102.0     49.9      0.0         1.0   \n",
       "\n",
       "         订单中的商品数量  订单应付总金额  订单实付金额  订单优惠金额     商品原始价格  \n",
       "0             1.0     96.9    96.9   143.0  54.909289  \n",
       "1             1.0      0.0     0.0    99.0  45.961352  \n",
       "2             1.0     96.9    96.9    92.1  53.035439  \n",
       "3             3.0     89.7    89.7    90.3  46.046917  \n",
       "4             1.0     65.9    65.9    94.0  50.722161  \n",
       "...           ...      ...     ...     ...        ...  \n",
       "2306866       1.0     59.9    59.9   139.1  53.012016  \n",
       "2306867       1.0      0.0     0.0    59.9  42.693822  \n",
       "2306868       1.0     89.9    89.9   150.0  54.889036  \n",
       "2306869       1.0     76.9    76.9    91.1  52.078004  \n",
       "2306870       1.0     49.9    49.9    52.1  46.114765  \n",
       "\n",
       "[2306871 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对价格，购买数量的特征单独出来，看看他们之间的关系\n",
    "amount_data = pd.DataFrame()\n",
    "amount_data['订单ID'] = data.order_id\n",
    "amount_data['订单详细ID'] = data.order_detail_id\n",
    "amount_data['订单商品总购买数量'] = data.order_total_num\n",
    "amount_data['订单商品总金额'] = data.order_amount\n",
    "amount_data['订单总实付金额'] = data.order_total_payment\n",
    "amount_data['订单总优惠金额'] = data.order_total_discount\n",
    "amount_data['订单包含的子订单数量'] = data.order_count\n",
    "amount_data['订单中的商品数量'] = data.order_detail_goods_num\n",
    "amount_data['订单应付总金额'] = data.order_detail_amount\n",
    "amount_data['订单实付金额'] = data.order_detail_payment\n",
    "amount_data['订单优惠金额'] = data.order_detail_discount\n",
    "amount_data['商品原始价格'] = data.goods_price\n",
    "amount_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-17T08:56:34.396381Z",
     "start_time": "2020-10-17T08:56:34.344378Z"
    }
   },
   "source": [
    "从上表中可以看出  \n",
    "- 订单ID + 订单详细ID可以说是表的key，能唯一确定一条记录，要是设置index的话，订单ID + 订单详细ID比较合适。\n",
    "- 订单ID可以重复，一张订单里有多种物品的话，就会有多个订单详细ID\n",
    "- 每条记录代表用户购买的一种商品。 同一个订单ID，不同订单详细ID表示在一次用户购物行为时，购买了多个物品，每一种物品会是一条记录。\n",
    "- 订单商品总金额 = 订单实付金额 + 订单优惠金额， 如果一个订单ID有多条记录，则等式右边是多条记录的累加。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:46:30.494585Z",
     "start_time": "2020-10-18T00:46:18.578904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>freq</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1329697</td>\n",
       "      <td>2012-11-11 00:00:19</td>\n",
       "      <td>1011</td>\n",
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       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <td>2306871</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2089352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_customer_rate</th>\n",
       "      <td>2.30687e+06</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.23440e+06</td>\n",
       "    </tr>\n",
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       "      <td>2.30687e+06</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.08293e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_province</th>\n",
       "      <td>2305732</td>\n",
       "      <td>33</td>\n",
       "      <td>广东省</td>\n",
       "      <td>255700</td>\n",
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       "    <tr>\n",
       "      <th>customer_city</th>\n",
       "      <td>2305721</td>\n",
       "      <td>375</td>\n",
       "      <td>上海市</td>\n",
       "      <td>117625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_gender</th>\n",
       "      <td>635790</td>\n",
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       "      <td>603970</td>\n",
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       "    <tr>\n",
       "      <th>member_status</th>\n",
       "      <td>635790</td>\n",
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       "      <td>603970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_member_actived</th>\n",
       "      <td>635790</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>635790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_status</th>\n",
       "      <td>2.30687e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.92985e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_list_time</th>\n",
       "      <td>2306871</td>\n",
       "      <td>925</td>\n",
       "      <td>2014-10-25 11:08:07</td>\n",
       "      <td>472943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goods_delist_time</th>\n",
       "      <td>2306871</td>\n",
       "      <td>884</td>\n",
       "      <td>2014-11-01 11:08:07</td>\n",
       "      <td>472943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count   unique                  top         freq\n",
       "order_pay_time           2306871  1329697  2012-11-11 00:00:19         1011\n",
       "order_status             2306871        6                    6      2089352\n",
       "is_customer_rate     2.30687e+06        2                    0  2.23440e+06\n",
       "order_detail_status  2.30687e+06        6                    6  2.08293e+06\n",
       "customer_province        2305732       33                  广东省       255700\n",
       "customer_city            2305721      375                  上海市       117625\n",
       "customer_gender           635790        3                    0       603970\n",
       "member_status             635790        3                    0       603970\n",
       "is_member_actived         635790        1                    1       635790\n",
       "goods_status         2.30687e+06        3                    1  1.92985e+06\n",
       "goods_list_time          2306871      925  2014-10-25 11:08:07       472943\n",
       "goods_delist_time        2306871      884  2014-11-01 11:08:07       472943"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='O').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "空值个数统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T00:48:18.959789Z",
     "start_time": "2020-10-18T00:48:15.198574Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_detail_id                 0\n",
       "order_id                        0\n",
       "order_total_num                 0\n",
       "order_amount                    0\n",
       "order_total_payment             0\n",
       "order_total_discount            0\n",
       "order_pay_time                  0\n",
       "order_status                    0\n",
       "order_count                     0\n",
       "is_customer_rate                0\n",
       "order_detail_status             0\n",
       "order_detail_goods_num          0\n",
       "order_detail_amount             0\n",
       "order_detail_payment            0\n",
       "order_detail_discount           0\n",
       "customer_province            1139\n",
       "customer_city                1150\n",
       "member_id                       0\n",
       "customer_id                     0\n",
       "customer_gender           1671081\n",
       "member_status             1671081\n",
       "is_member_actived         1671081\n",
       "goods_id                        0\n",
       "goods_class_id                  0\n",
       "goods_price                   436\n",
       "goods_status                    0\n",
       "goods_has_discount              0\n",
       "goods_list_time                 0\n",
       "goods_delist_time               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的数据，可以看出缺失值是用户的一些信息，及商品的原始价格，先暂时不用填充。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每个特征进行分析\n",
    "0   order_detail_id         int64  \n",
    " 1   order_id                int64  \n",
    " 2   order_total_num         float64\n",
    " 3   order_amount            float64\n",
    " 4   order_total_payment     float64\n",
    " 5   order_total_discount    float64\n",
    " 6   order_pay_time          object \n",
    " 7   order_status            int64  \n",
    " 8   order_count             float64\n",
    " 9   is_customer_rate        float64\n",
    " 10  order_detail_status     float64\n",
    " 11  order_detail_goods_num  float64\n",
    " 12  order_detail_amount     float64\n",
    " 13  order_detail_payment    float64\n",
    " 14  order_detail_discount   float64\n",
    " 15  customer_province       object \n",
    " 16  customer_city           object \n",
    " 17  member_id               float64\n",
    " 18  customer_id             int64  \n",
    " 19  customer_gender         float64\n",
    " 20  member_status           float64\n",
    " 21  is_member_actived       float64\n",
    " 22  goods_id                int64  \n",
    " 23  goods_class_id          int64  \n",
    " 24  goods_price             float64\n",
    " 25  goods_status            float64\n",
    " 26  goods_has_discount      float64\n",
    " 27  goods_list_time         object \n",
    " 28  goods_delist_time       object "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "order_total_num\n",
    "order_amount\n",
    "order_total_payment\n",
    "order_total_discount\n",
    "order_count\n",
    "order_detail_goods_num\n",
    "order_detail_amount\n",
    "order_detail_payment\n",
    "order_detail_discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:15:05.878508Z",
     "start_time": "2020-10-18T01:14:44.261271Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(5,2,figsize=(18,10))\n",
    "sns.boxplot(data['order_total_num'],ax=axs[0,0])\n",
    "sns.boxplot(data['order_amount'],ax=axs[0,1])\n",
    "\n",
    "sns.boxplot(data['order_total_payment'],ax=axs[1,0])\n",
    "sns.boxplot(data['order_total_discount'],ax=axs[1,1])\n",
    "\n",
    "sns.boxplot(data['order_count'],ax=axs[2,0])\n",
    "sns.boxplot(data['order_detail_goods_num'],ax=axs[2,1])\n",
    "\n",
    "sns.boxplot(data['order_detail_amount'],ax=axs[3,0])\n",
    "sns.boxplot(data['order_detail_payment'],ax=axs[3,1])\n",
    "\n",
    "sns.boxplot(data['order_detail_discount'],ax=axs[4,0])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:46:45.387103Z",
     "start_time": "2020-10-18T01:46:43.350987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_total_num'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(figsize=(18,10))\n",
    "sns.boxplot(data['order_total_num'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:47:17.668950Z",
     "start_time": "2020-10-18T01:47:17.649948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1700.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['order_total_num'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:48:37.272503Z",
     "start_time": "2020-10-18T01:48:37.161496Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>order_detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_total_num</th>\n",
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       "      <td>2014-10-28 11:30:18</td>\n",
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       "      <td>广东省</td>\n",
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       "      <td>1700.0</td>\n",
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       "         order_detail_id  order_id  order_total_num  order_amount  \\\n",
       "585865           1680363   1580667           1690.0        1690.0   \n",
       "805432           1935674   1798726           1438.0        1438.0   \n",
       "2284893          3658881   3217575           1700.0        1700.0   \n",
       "\n",
       "         order_total_payment  order_total_discount       order_pay_time  \\\n",
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       "\n",
       "        order_status  order_count is_customer_rate order_detail_status  \\\n",
       "585865             6          1.0                0                   6   \n",
       "805432             6          1.0                0                   6   \n",
       "2284893            6          1.0                0                   6   \n",
       "\n",
       "         order_detail_goods_num  order_detail_amount  order_detail_payment  \\\n",
       "585865                   1690.0               1690.0                1690.0   \n",
       "805432                   1438.0               1438.0                1438.0   \n",
       "2284893                  1700.0               1700.0                1700.0   \n",
       "\n",
       "         order_detail_discount customer_province customer_city  member_id  \\\n",
       "585865                     0.0               广东省           东莞市        0.0   \n",
       "805432                     0.0               广东省           佛山市        0.0   \n",
       "2284893                    0.0               江西省           赣州市  2730081.0   \n",
       "\n",
       "         customer_id customer_gender member_status is_member_actived  \\\n",
       "585865       1390567             NaN           NaN               NaN   \n",
       "805432       1736061             NaN           NaN               NaN   \n",
       "2284893      2812033               0             0                 1   \n",
       "\n",
       "         goods_id  goods_class_id  goods_price goods_status  \\\n",
       "585865       1768            1768   -16.834691            1   \n",
       "805432       1768            1768     5.610210            1   \n",
       "2284893      1768            1768          NaN            1   \n",
       "\n",
       "         goods_has_discount      goods_list_time    goods_delist_time  \n",
       "585865                  0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "805432                  0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "2284893                 0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['order_total_num']>=1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:48:53.217415Z",
     "start_time": "2020-10-18T01:48:53.080407Z"
    }
   },
   "outputs": [
    {
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       "      <td>1741161</td>\n",
       "      <td>639.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>500.0</td>\n",
       "      <td>99500.0</td>\n",
       "      <td>44950.0</td>\n",
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       "      <td>6178</td>\n",
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       "      <td>2012-12-02 15:19:26</td>\n",
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       "      <td>2381108</td>\n",
       "      <td>881.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-04-03 12:07:49</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>881.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>南通市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1107336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1768</td>\n",
       "      <td>1768</td>\n",
       "      <td>14.703128</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-28 11:30:18</td>\n",
       "      <td>2014-11-04 11:30:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894714</th>\n",
       "      <td>3200741</td>\n",
       "      <td>2858937</td>\n",
       "      <td>500.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-08-11 12:47:00</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>福建省</td>\n",
       "      <td>福州市</td>\n",
       "      <td>2757063.0</td>\n",
       "      <td>2563394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1793</td>\n",
       "      <td>1793</td>\n",
       "      <td>50.870022</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-23 15:02:07</td>\n",
       "      <td>2014-10-30 15:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082294</th>\n",
       "      <td>3421507</td>\n",
       "      <td>3031577</td>\n",
       "      <td>520.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-02-13 18:30:36</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>520.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>浙江省</td>\n",
       "      <td>宁波市</td>\n",
       "      <td>1205056.0</td>\n",
       "      <td>2683563</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1768</td>\n",
       "      <td>1768</td>\n",
       "      <td>4.785306</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-28 11:30:18</td>\n",
       "      <td>2014-11-04 11:30:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284893</th>\n",
       "      <td>3658881</td>\n",
       "      <td>3217575</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-03-20 10:04:21</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>江西省</td>\n",
       "      <td>赣州市</td>\n",
       "      <td>2730081.0</td>\n",
       "      <td>2812033</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1768</td>\n",
       "      <td>1768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-10-28 11:30:18</td>\n",
       "      <td>2014-11-04 11:30:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_detail_id  order_id  order_total_num  order_amount  \\\n",
       "585865           1680363   1580667           1690.0        1690.0   \n",
       "701282           1814443   1695142            985.0       98401.5   \n",
       "747535           1868405   1741161            639.0         639.0   \n",
       "805432           1935674   1798726           1438.0        1438.0   \n",
       "989236           2148828   1980543            879.0         879.0   \n",
       "1058086          2227526   2047300            500.0       99500.0   \n",
       "1058087          2227527   2047300            500.0       99500.0   \n",
       "1393922          2618610   2381108            881.0         881.0   \n",
       "1894714          3200741   2858937            500.0       24500.0   \n",
       "2082294          3421507   3031577            520.0         520.0   \n",
       "2284893          3658881   3217575           1700.0        1700.0   \n",
       "\n",
       "         order_total_payment  order_total_discount       order_pay_time  \\\n",
       "585865                1690.0                   0.0  2012-12-30 20:38:14   \n",
       "701282               98401.5                   0.0  2013-01-19 17:56:30   \n",
       "747535                 639.0                   0.0  2013-02-27 20:44:47   \n",
       "805432                1438.0                   0.0  2013-02-28 23:52:06   \n",
       "989236                 879.0                   0.0  2013-02-12 17:46:37   \n",
       "1058086              44950.0                   0.0  2012-11-24 14:27:09   \n",
       "1058087              44950.0                   0.0  2012-11-24 14:27:09   \n",
       "1393922                881.0                   0.0  2013-04-03 12:07:49   \n",
       "1894714              11950.0                   0.0  2013-08-11 12:47:00   \n",
       "2082294                520.0                   0.0  2013-02-13 18:30:36   \n",
       "2284893               1700.0                   0.0  2013-03-20 10:04:21   \n",
       "\n",
       "        order_status  order_count is_customer_rate order_detail_status  \\\n",
       "585865             6          1.0                0                   6   \n",
       "701282             6          1.0                0                   6   \n",
       "747535             6          1.0                0                   6   \n",
       "805432             6          1.0                0                   6   \n",
       "989236             6          1.0                0                   6   \n",
       "1058086            6          2.0                0                   6   \n",
       "1058087            6          2.0                0                   6   \n",
       "1393922            6          1.0                0                   6   \n",
       "1894714            6          1.0                0                   6   \n",
       "2082294            6          1.0                0                   6   \n",
       "2284893            6          1.0                0                   6   \n",
       "\n",
       "         order_detail_goods_num  order_detail_amount  order_detail_payment  \\\n",
       "585865                   1690.0               1690.0                1690.0   \n",
       "701282                    985.0              98401.5               98401.5   \n",
       "747535                    639.0                639.0                 639.0   \n",
       "805432                   1438.0               1438.0                1438.0   \n",
       "989236                    879.0                879.0                 879.0   \n",
       "1058086                   350.0              31465.0               31465.0   \n",
       "1058087                   150.0              13485.0               13485.0   \n",
       "1393922                   881.0                881.0                 881.0   \n",
       "1894714                   500.0              11950.0               11950.0   \n",
       "2082294                   520.0                520.0                 520.0   \n",
       "2284893                  1700.0               1700.0                1700.0   \n",
       "\n",
       "         order_detail_discount customer_province customer_city  member_id  \\\n",
       "585865                     0.0               广东省           东莞市        0.0   \n",
       "701282                     0.0               湖南省           长沙市        0.0   \n",
       "747535                     0.0               浙江省           杭州市        0.0   \n",
       "805432                     0.0               广东省           佛山市        0.0   \n",
       "989236                     0.0               江苏省           苏州市        0.0   \n",
       "1058086                38185.0               山西省           大同市        0.0   \n",
       "1058087                16365.0               山西省           大同市        0.0   \n",
       "1393922                    0.0               江苏省           南通市        0.0   \n",
       "1894714                12550.0               福建省           福州市  2757063.0   \n",
       "2082294                    0.0               浙江省           宁波市  1205056.0   \n",
       "2284893                    0.0               江西省           赣州市  2730081.0   \n",
       "\n",
       "         customer_id customer_gender member_status is_member_actived  \\\n",
       "585865       1390567             NaN           NaN               NaN   \n",
       "701282       1646629             NaN           NaN               NaN   \n",
       "747535       1686476             NaN           NaN               NaN   \n",
       "805432       1736061             NaN           NaN               NaN   \n",
       "989236       1467620             NaN           NaN               NaN   \n",
       "1058086      1844181             NaN           NaN               NaN   \n",
       "1058087      1844181             NaN           NaN               NaN   \n",
       "1393922      1107336             NaN           NaN               NaN   \n",
       "1894714      2563394               0             0                 1   \n",
       "2082294      2683563               0             0                 1   \n",
       "2284893      2812033               0             0                 1   \n",
       "\n",
       "         goods_id  goods_class_id  goods_price goods_status  \\\n",
       "585865       1768            1768   -16.834691            1   \n",
       "701282       3243            3243    46.107564            2   \n",
       "747535       1768            1768     7.321635            1   \n",
       "805432       1768            1768     5.610210            1   \n",
       "989236       1768            1768   -12.635918            1   \n",
       "1058086      6178            6178    53.059703            0   \n",
       "1058087      6178            6178    52.916829            0   \n",
       "1393922      1768            1768    14.703128            1   \n",
       "1894714      1793            1793    50.870022            1   \n",
       "2082294      1768            1768     4.785306            1   \n",
       "2284893      1768            1768          NaN            1   \n",
       "\n",
       "         goods_has_discount      goods_list_time    goods_delist_time  \n",
       "585865                  0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "701282                  0.0  2013-01-26 12:12:17  2013-01-29 14:40:39  \n",
       "747535                  0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "805432                  0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "989236                  0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "1058086                 0.0  2012-12-02 11:56:50  2012-12-02 15:19:26  \n",
       "1058087                 0.0  2012-12-02 11:56:50  2012-12-02 15:19:26  \n",
       "1393922                 0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "1894714                 0.0  2014-10-23 15:02:07  2014-10-30 15:02:07  \n",
       "2082294                 0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  \n",
       "2284893                 0.0  2014-10-28 11:30:18  2014-11-04 11:30:18  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['order_total_num']>=500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:49:46.234447Z",
     "start_time": "2020-10-18T01:49:46.209446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['order_total_num']>=500].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#大于500的有11个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[data['order_total_num']>=500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:51:16.627617Z",
     "start_time": "2020-10-18T01:51:13.128417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_total_num'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(figsize=(18,10))\n",
    "sns.boxplot(data[data['order_total_num']<500]['order_total_num'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T01:52:55.319262Z",
     "start_time": "2020-10-18T01:52:55.250258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13367"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['order_total_num']>=10].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:06:02.402064Z",
     "start_time": "2020-10-18T02:04:53.954149Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_total_num'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(figsize=(30,10))\n",
    "sns.distplot(data['order_total_num'], bins=100, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:10:33.123496Z",
     "start_time": "2020-10-18T02:10:31.469401Z"
    }
   },
   "outputs": [],
   "source": [
    "below_500 = data[data['order_total_num']<500]['order_total_num']\n",
    "above_500 = data[data['order_total_num']>=500]['order_total_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:12:51.290399Z",
     "start_time": "2020-10-18T02:10:53.393655Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_total_num'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.index = data.order_detail_id\n",
    "fig,axs = plt.subplots(figsize=(30,10))\n",
    "\n",
    "sns.distplot(below_500, bins=100, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(below_500, bins=100, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.index = data.order_detail_id\n",
    "fig,axs = plt.subplots(figsize=(30,10))\n",
    "\n",
    "sns.distplot(above_500, bins=20, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:20:52.156903Z",
     "start_time": "2020-10-18T02:20:52.035896Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0      1523387\n",
       "2.0       526933\n",
       "3.0       127457\n",
       "4.0        56633\n",
       "5.0        27189\n",
       "          ...   \n",
       "203.0          1\n",
       "201.0          1\n",
       "106.0          1\n",
       "198.0          1\n",
       "115.0          1\n",
       "Name: order_total_num, Length: 239, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = data['order_total_num'].value_counts()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:25:44.457621Z",
     "start_time": "2020-10-18T02:25:44.444621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(tmp!=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:26:29.381191Z",
     "start_time": "2020-10-18T02:26:29.361190Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0      1523387\n",
       "2.0       526933\n",
       "3.0       127457\n",
       "4.0        56633\n",
       "5.0        27189\n",
       "          ...   \n",
       "142.0          2\n",
       "105.0          2\n",
       "143.0          2\n",
       "146.0          2\n",
       "81.0           2\n",
       "Name: order_total_num, Length: 164, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp2 = tmp[tmp!=1]\n",
    "tmp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:27:11.435596Z",
     "start_time": "2020-10-18T02:27:08.681439Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_total_num', ylabel='count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(figsize=(30,10))\n",
    "sns.countplot(tmp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T02:33:43.038995Z",
     "start_time": "2020-10-18T02:33:42.968991Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['order_total_num']>=10].shape[0] / data.shape[0] * 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "订单商品总购买数量,基本都是个位数，估计采购比较大的是单位采购，或者批发采购"
   ]
  }
 ],
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